Overview

Dataset statistics

Number of variables17
Number of observations46651
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 MiB
Average record size in memory136.0 B

Variable types

Categorical7
Numeric10

Alerts

TimeStamp is highly correlated with GazePointYHigh correlation
GazePointXLeft is highly correlated with ValidityLeft and 3 other fieldsHigh correlation
GazePointYLeft is highly correlated with ValidityLeft and 3 other fieldsHigh correlation
ValidityLeft is highly correlated with GazePointXLeft and 5 other fieldsHigh correlation
GazePointXRight is highly correlated with GazePointXLeft and 3 other fieldsHigh correlation
GazePointYRight is highly correlated with GazePointYLeft and 3 other fieldsHigh correlation
ValidityRight is highly correlated with ValidityLeft and 5 other fieldsHigh correlation
GazePointX is highly correlated with GazePointXLeft and 1 other fieldsHigh correlation
GazePointY is highly correlated with TimeStamp and 2 other fieldsHigh correlation
PupilSizeLeft is highly correlated with ValidityLeft and 2 other fieldsHigh correlation
PupilValidityLeft is highly correlated with GazePointXLeft and 5 other fieldsHigh correlation
PupilSizeRight is highly correlated with ValidityRight and 2 other fieldsHigh correlation
PupilValidityRight is highly correlated with ValidityLeft and 5 other fieldsHigh correlation
TimeStamp is highly correlated with GazePointYHigh correlation
GazePointXLeft is highly correlated with ValidityLeft and 4 other fieldsHigh correlation
GazePointYLeft is highly correlated with ValidityLeft and 4 other fieldsHigh correlation
ValidityLeft is highly correlated with GazePointXLeft and 6 other fieldsHigh correlation
GazePointXRight is highly correlated with GazePointXLeft and 4 other fieldsHigh correlation
GazePointYRight is highly correlated with GazePointYLeft and 4 other fieldsHigh correlation
ValidityRight is highly correlated with ValidityLeft and 6 other fieldsHigh correlation
GazePointX is highly correlated with GazePointXLeft and 1 other fieldsHigh correlation
GazePointY is highly correlated with TimeStamp and 2 other fieldsHigh correlation
PupilSizeLeft is highly correlated with GazePointXLeft and 6 other fieldsHigh correlation
PupilValidityLeft is highly correlated with GazePointXLeft and 6 other fieldsHigh correlation
PupilSizeRight is highly correlated with ValidityLeft and 6 other fieldsHigh correlation
PupilValidityRight is highly correlated with ValidityLeft and 6 other fieldsHigh correlation
GazePointXLeft is highly correlated with ValidityLeft and 3 other fieldsHigh correlation
GazePointYLeft is highly correlated with ValidityLeft and 3 other fieldsHigh correlation
ValidityLeft is highly correlated with GazePointXLeft and 5 other fieldsHigh correlation
GazePointXRight is highly correlated with GazePointXLeft and 3 other fieldsHigh correlation
GazePointYRight is highly correlated with GazePointYLeft and 3 other fieldsHigh correlation
ValidityRight is highly correlated with ValidityLeft and 5 other fieldsHigh correlation
GazePointX is highly correlated with GazePointXLeft and 1 other fieldsHigh correlation
GazePointY is highly correlated with GazePointYLeft and 1 other fieldsHigh correlation
PupilSizeLeft is highly correlated with ValidityLeft and 2 other fieldsHigh correlation
PupilValidityLeft is highly correlated with GazePointXLeft and 5 other fieldsHigh correlation
PupilSizeRight is highly correlated with ValidityRight and 2 other fieldsHigh correlation
PupilValidityRight is highly correlated with ValidityLeft and 5 other fieldsHigh correlation
PupilValidityRight is highly correlated with ValidityLeft and 2 other fieldsHigh correlation
diagnosis is highly correlated with class and 1 other fieldsHigh correlation
class is highly correlated with diagnosis and 1 other fieldsHigh correlation
ValidityLeft is highly correlated with PupilValidityRight and 2 other fieldsHigh correlation
id is highly correlated with diagnosis and 1 other fieldsHigh correlation
ValidityRight is highly correlated with PupilValidityRight and 2 other fieldsHigh correlation
PupilValidityLeft is highly correlated with PupilValidityRight and 2 other fieldsHigh correlation
id is highly correlated with Age and 6 other fieldsHigh correlation
TimeStamp is highly correlated with GazePointYLeft and 2 other fieldsHigh correlation
Age is highly correlated with id and 2 other fieldsHigh correlation
GazePointXLeft is highly correlated with GazePointYLeft and 5 other fieldsHigh correlation
GazePointYLeft is highly correlated with TimeStamp and 9 other fieldsHigh correlation
ValidityLeft is highly correlated with GazePointXLeft and 10 other fieldsHigh correlation
GazePointXRight is highly correlated with GazePointXLeft and 8 other fieldsHigh correlation
GazePointYRight is highly correlated with TimeStamp and 8 other fieldsHigh correlation
ValidityRight is highly correlated with id and 10 other fieldsHigh correlation
GazePointX is highly correlated with GazePointXLeft and 7 other fieldsHigh correlation
GazePointY is highly correlated with TimeStamp and 7 other fieldsHigh correlation
PupilSizeLeft is highly correlated with id and 7 other fieldsHigh correlation
PupilValidityLeft is highly correlated with GazePointXLeft and 10 other fieldsHigh correlation
PupilSizeRight is highly correlated with id and 8 other fieldsHigh correlation
PupilValidityRight is highly correlated with id and 10 other fieldsHigh correlation
class is highly correlated with id and 2 other fieldsHigh correlation
diagnosis is highly correlated with id and 2 other fieldsHigh correlation

Reproduction

Analysis started2022-06-04 06:12:48.423709
Analysis finished2022-06-04 06:13:08.308101
Duration19.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size364.6 KiB
9-2
 
2113
9-1
 
2094
4-2
 
2063
2-2
 
2057
1-2
 
2030
Other values (20)
36294 

Length

Max length4
Median length3
Mean length3.285117146
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-1
2nd row1-1
3rd row1-1
4th row1-1
5th row1-1

Common Values

ValueCountFrequency (%)
9-22113
 
4.5%
9-12094
 
4.5%
4-22063
 
4.4%
2-22057
 
4.4%
1-22030
 
4.4%
4-12016
 
4.3%
2-11993
 
4.3%
1-11985
 
4.3%
12-31955
 
4.2%
10-21949
 
4.2%
Other values (15)26396
56.6%

Length

2022-06-04T08:13:08.376497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9-22113
 
4.5%
9-12094
 
4.5%
4-22063
 
4.4%
2-22057
 
4.4%
1-22030
 
4.4%
4-12016
 
4.3%
2-11993
 
4.3%
1-11985
 
4.3%
12-31955
 
4.2%
10-21949
 
4.2%
Other values (15)26396
56.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TimeStamp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46620
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27202.04959
Minimum142.02
Maximum54521.708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size364.6 KiB
2022-06-04T08:13:08.488358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum142.02
5-th percentile2774.2455
Q113681.465
median27261.161
Q340658.198
95-th percentile51694.417
Maximum54521.708
Range54379.688
Interquartile range (IQR)26976.733

Descriptive statistics

Standard deviation15631.74659
Coefficient of variation (CV)0.574653264
Kurtosis-1.190021689
Mean27202.04959
Median Absolute Deviation (MAD)13486.862
Skewness0.0003255177304
Sum1269002816
Variance244351501.4
MonotonicityNot monotonic
2022-06-04T08:13:08.645177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41938.8212
 
< 0.1%
20032.2342
 
< 0.1%
48628.3042
 
< 0.1%
49324.2352
 
< 0.1%
4838.662
 
< 0.1%
30166.2882
 
< 0.1%
2434.1482
 
< 0.1%
48350.2712
 
< 0.1%
31721.0782
 
< 0.1%
13112.4732
 
< 0.1%
Other values (46610)46631
> 99.9%
ValueCountFrequency (%)
142.021
< 0.1%
155.7451
< 0.1%
176.7681
< 0.1%
191.8891
< 0.1%
196.9541
< 0.1%
199.3961
< 0.1%
208.1761
< 0.1%
208.3421
< 0.1%
208.7691
< 0.1%
208.8181
< 0.1%
ValueCountFrequency (%)
54521.7081
< 0.1%
54518.3651
< 0.1%
54517.8021
< 0.1%
54516.1541
< 0.1%
54509.8531
< 0.1%
54509.3911
< 0.1%
54507.4261
< 0.1%
54505.7691
< 0.1%
54504.9781
< 0.1%
54503.6021
< 0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.28886841
Minimum17
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size364.6 KiB
2022-06-04T08:13:08.753206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17
Q145
median50
Q369
95-th percentile72
Maximum72
Range55
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.84613123
Coefficient of variation (CV)0.3221743316
Kurtosis-0.1741097958
Mean52.28886841
Median Absolute Deviation (MAD)7
Skewness-0.72350509
Sum2439328
Variance283.7921374
MonotonicityNot monotonic
2022-06-04T08:13:08.837439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
728076
17.3%
175697
12.2%
454079
8.7%
434050
8.7%
564015
8.6%
483856
8.3%
653773
8.1%
693749
8.0%
503735
8.0%
463615
7.7%
ValueCountFrequency (%)
175697
12.2%
434050
8.7%
454079
8.7%
463615
7.7%
483856
8.3%
503735
8.0%
564015
8.6%
653773
8.1%
682006
 
4.3%
693749
8.0%
ValueCountFrequency (%)
728076
17.3%
693749
8.0%
682006
 
4.3%
653773
8.1%
564015
8.6%
503735
8.0%
483856
8.3%
463615
7.7%
454079
8.7%
434050
8.7%

GazePointXLeft
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2059
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean719.8776875
Minimum-1299
Maximum2641
Zeros0
Zeros (%)0.0%
Negative9213
Negative (%)19.7%
Memory size364.6 KiB
2022-06-04T08:13:08.949468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1299
5-th percentile-1
Q1210
median694
Q31176
95-th percentile1634
Maximum2641
Range3940
Interquartile range (IQR)966

Descriptive statistics

Standard deviation554.2085776
Coefficient of variation (CV)0.769864919
Kurtosis-1.183030522
Mean719.8776875
Median Absolute Deviation (MAD)483
Skewness0.1817716833
Sum33583014
Variance307147.1475
MonotonicityNot monotonic
2022-06-04T08:13:09.065500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18939
 
19.2%
62142
 
0.1%
117140
 
0.1%
72640
 
0.1%
45440
 
0.1%
112940
 
0.1%
93039
 
0.1%
37339
 
0.1%
52639
 
0.1%
43739
 
0.1%
Other values (2049)37354
80.1%
ValueCountFrequency (%)
-12991
< 0.1%
-5771
< 0.1%
-5591
< 0.1%
-4861
< 0.1%
-4761
< 0.1%
-4721
< 0.1%
-4711
< 0.1%
-4591
< 0.1%
-4531
< 0.1%
-4441
< 0.1%
ValueCountFrequency (%)
26411
< 0.1%
25391
< 0.1%
25231
< 0.1%
23121
< 0.1%
20871
< 0.1%
20761
< 0.1%
20351
< 0.1%
20321
< 0.1%
20221
< 0.1%
19981
< 0.1%

GazePointYLeft
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1246
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385.0036226
Minimum-1006
Maximum1749
Zeros1
Zeros (%)< 0.1%
Negative9069
Negative (%)19.4%
Memory size364.6 KiB
2022-06-04T08:13:09.185534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1006
5-th percentile-1
Q1130
median386
Q3589
95-th percentile870
Maximum1749
Range2755
Interquartile range (IQR)459

Descriptive statistics

Standard deviation289.2443707
Coefficient of variation (CV)0.7512770107
Kurtosis-0.9157462401
Mean385.0036226
Median Absolute Deviation (MAD)235
Skewness0.2022064045
Sum17960804
Variance83662.30599
MonotonicityNot monotonic
2022-06-04T08:13:09.309562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18939
 
19.2%
476100
 
0.2%
47196
 
0.2%
48590
 
0.2%
48988
 
0.2%
47987
 
0.2%
47485
 
0.2%
48285
 
0.2%
31284
 
0.2%
56083
 
0.2%
Other values (1236)36914
79.1%
ValueCountFrequency (%)
-10061
< 0.1%
-7291
< 0.1%
-7061
< 0.1%
-6671
< 0.1%
-6601
< 0.1%
-6211
< 0.1%
-5061
< 0.1%
-4741
< 0.1%
-4731
< 0.1%
-4561
< 0.1%
ValueCountFrequency (%)
17491
< 0.1%
14921
< 0.1%
14361
< 0.1%
13671
< 0.1%
13641
< 0.1%
13621
< 0.1%
13611
< 0.1%
13591
< 0.1%
13571
< 0.1%
13551
< 0.1%

ValidityLeft
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size364.6 KiB
1
37713 
0
8938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
137713
80.8%
08938
 
19.2%

Length

2022-06-04T08:13:09.418539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T08:13:09.474554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
137713
80.8%
08938
 
19.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GazePointXRight
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2009
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739.7571328
Minimum-592
Maximum2560
Zeros0
Zeros (%)0.0%
Negative6932
Negative (%)14.9%
Memory size364.6 KiB
2022-06-04T08:13:09.550820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-592
5-th percentile-1
Q1306
median708
Q31151
95-th percentile1613
Maximum2560
Range3152
Interquartile range (IQR)845

Descriptive statistics

Standard deviation522.0729168
Coefficient of variation (CV)0.7057355633
Kurtosis-1.057256214
Mean739.7571328
Median Absolute Deviation (MAD)425
Skewness0.1760175269
Sum34510410
Variance272560.1304
MonotonicityNot monotonic
2022-06-04T08:13:09.675476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-16787
 
14.5%
52750
 
0.1%
51946
 
0.1%
51145
 
0.1%
95545
 
0.1%
44845
 
0.1%
53045
 
0.1%
53245
 
0.1%
71744
 
0.1%
35743
 
0.1%
Other values (1999)39456
84.6%
ValueCountFrequency (%)
-5921
< 0.1%
-4621
< 0.1%
-4481
< 0.1%
-4471
< 0.1%
-2451
< 0.1%
-2361
< 0.1%
-2321
< 0.1%
-2281
< 0.1%
-2191
< 0.1%
-2151
< 0.1%
ValueCountFrequency (%)
25601
< 0.1%
24061
< 0.1%
23461
< 0.1%
22671
< 0.1%
22541
< 0.1%
21371
< 0.1%
20771
< 0.1%
20741
< 0.1%
20431
< 0.1%
20191
< 0.1%

GazePointYRight
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1204
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean401.0919166
Minimum-805
Maximum1613
Zeros5
Zeros (%)< 0.1%
Negative6916
Negative (%)14.8%
Memory size364.6 KiB
2022-06-04T08:13:09.807696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-805
5-th percentile-1
Q1168
median403
Q3596
95-th percentile858
Maximum1613
Range2418
Interquartile range (IQR)428

Descriptive statistics

Standard deviation277.5437254
Coefficient of variation (CV)0.6919703788
Kurtosis-0.8955670648
Mean401.0919166
Median Absolute Deviation (MAD)217
Skewness0.1359666544
Sum18711339
Variance77030.51953
MonotonicityNot monotonic
2022-06-04T08:13:09.935961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-16790
 
14.6%
49290
 
0.2%
48588
 
0.2%
49387
 
0.2%
50085
 
0.2%
49485
 
0.2%
48184
 
0.2%
48684
 
0.2%
52784
 
0.2%
48884
 
0.2%
Other values (1194)39090
83.8%
ValueCountFrequency (%)
-8051
< 0.1%
-7431
< 0.1%
-7331
< 0.1%
-7131
< 0.1%
-6871
< 0.1%
-6821
< 0.1%
-6741
< 0.1%
-6351
< 0.1%
-3621
< 0.1%
-3401
< 0.1%
ValueCountFrequency (%)
16131
< 0.1%
13921
< 0.1%
13751
< 0.1%
13611
< 0.1%
13601
< 0.1%
13451
< 0.1%
13081
< 0.1%
12861
< 0.1%
12381
< 0.1%
12171
< 0.1%

ValidityRight
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size364.6 KiB
1
39865 
0
6786 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
139865
85.5%
06786
 
14.5%

Length

2022-06-04T08:13:10.039888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T08:13:10.095770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
139865
85.5%
06786
 
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GazePointX
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2021
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773.4971812
Minimum-1299
Maximum2312
Zeros0
Zeros (%)0.0%
Negative5688
Negative (%)12.2%
Memory size364.6 KiB
2022-06-04T08:13:10.168018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1299
5-th percentile-1
Q1344
median749
Q31190
95-th percentile1631
Maximum2312
Range3611
Interquartile range (IQR)846

Descriptive statistics

Standard deviation522.3069377
Coefficient of variation (CV)0.6752538346
Kurtosis-1.086847233
Mean773.4971812
Median Absolute Deviation (MAD)424
Skewness0.1160302006
Sum36084417
Variance272804.5372
MonotonicityNot monotonic
2022-06-04T08:13:10.275817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-15441
 
11.7%
91848
 
0.1%
73046
 
0.1%
109946
 
0.1%
126745
 
0.1%
52745
 
0.1%
52945
 
0.1%
47044
 
0.1%
38043
 
0.1%
44943
 
0.1%
Other values (2011)40805
87.5%
ValueCountFrequency (%)
-12991
< 0.1%
-5921
< 0.1%
-4621
< 0.1%
-4481
< 0.1%
-4471
< 0.1%
-3411
< 0.1%
-3311
< 0.1%
-3221
< 0.1%
-3041
< 0.1%
-3001
< 0.1%
ValueCountFrequency (%)
23121
< 0.1%
21491
< 0.1%
20871
< 0.1%
20851
< 0.1%
19771
< 0.1%
19761
< 0.1%
19751
< 0.1%
19561
< 0.1%
19551
< 0.1%
19511
< 0.1%

GazePointY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1213
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.9040321
Minimum-706
Maximum1613
Zeros6
Zeros (%)< 0.1%
Negative5536
Negative (%)11.9%
Memory size364.6 KiB
2022-06-04T08:13:10.399852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-706
5-th percentile-1
Q1201
median420
Q3619
95-th percentile861
Maximum1613
Range2319
Interquartile range (IQR)418

Descriptive statistics

Standard deviation273.1034459
Coefficient of variation (CV)0.6519475226
Kurtosis-0.8530214381
Mean418.9040321
Median Absolute Deviation (MAD)212
Skewness0.08911208844
Sum19542292
Variance74585.49216
MonotonicityNot monotonic
2022-06-04T08:13:10.523891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-15441
 
11.7%
483105
 
0.2%
494100
 
0.2%
50199
 
0.2%
49098
 
0.2%
48198
 
0.2%
55496
 
0.2%
48896
 
0.2%
48296
 
0.2%
31595
 
0.2%
Other values (1203)40327
86.4%
ValueCountFrequency (%)
-7061
< 0.1%
-6191
< 0.1%
-5711
< 0.1%
-5681
< 0.1%
-5641
< 0.1%
-5621
< 0.1%
-5401
< 0.1%
-5391
< 0.1%
-5281
< 0.1%
-4741
< 0.1%
ValueCountFrequency (%)
16131
< 0.1%
14921
< 0.1%
14361
< 0.1%
13611
< 0.1%
13591
< 0.1%
13571
< 0.1%
13451
< 0.1%
13441
< 0.1%
13251
< 0.1%
13221
< 0.1%

PupilSizeLeft
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12536
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.636041487
Minimum-1
Maximum5.4947
Zeros0
Zeros (%)0.0%
Negative8938
Negative (%)19.2%
Memory size364.6 KiB
2022-06-04T08:13:10.640140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11.6745
median2.1457
Q32.4213
95-th percentile2.8042
Maximum5.4947
Range6.4947
Interquartile range (IQR)0.7468

Descriptive statistics

Standard deviation1.320657414
Coefficient of variation (CV)0.8072273381
Kurtosis0.171023376
Mean1.636041487
Median Absolute Deviation (MAD)0.3424
Skewness-1.357704961
Sum76322.9714
Variance1.744136006
MonotonicityNot monotonic
2022-06-04T08:13:10.751941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18938
 
19.2%
2.198716
 
< 0.1%
2.165613
 
< 0.1%
2.231613
 
< 0.1%
2.057813
 
< 0.1%
2.251313
 
< 0.1%
2.37313
 
< 0.1%
2.112413
 
< 0.1%
2.121413
 
< 0.1%
2.169112
 
< 0.1%
Other values (12526)37594
80.6%
ValueCountFrequency (%)
-18938
19.2%
1.21731
 
< 0.1%
1.27481
 
< 0.1%
1.29031
 
< 0.1%
1.30341
 
< 0.1%
1.30791
 
< 0.1%
1.31951
 
< 0.1%
1.3251
 
< 0.1%
1.33741
 
< 0.1%
1.35011
 
< 0.1%
ValueCountFrequency (%)
5.49471
< 0.1%
5.17411
< 0.1%
4.49631
< 0.1%
4.32581
< 0.1%
4.18071
< 0.1%
4.10451
< 0.1%
4.0481
< 0.1%
4.03161
< 0.1%
3.85051
< 0.1%
3.74531
< 0.1%

PupilValidityLeft
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size364.6 KiB
1
37713 
0
8938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
137713
80.8%
08938
 
19.2%

Length

2022-06-04T08:13:10.855979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T08:13:10.919985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
137713
80.8%
08938
 
19.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PupilSizeRight
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13129
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.852582586
Minimum-1
Maximum4.71
Zeros0
Zeros (%)0.0%
Negative6786
Negative (%)14.5%
Memory size364.6 KiB
2022-06-04T08:13:10.992823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11.90665
median2.2934
Q32.5125
95-th percentile2.8813
Maximum4.71
Range5.71
Interquartile range (IQR)0.60585

Descriptive statistics

Standard deviation1.22088025
Coefficient of variation (CV)0.6590152901
Kurtosis1.440268032
Mean1.852582586
Median Absolute Deviation (MAD)0.2746
Skewness-1.71935409
Sum86424.8302
Variance1.490548585
MonotonicityNot monotonic
2022-06-04T08:13:11.409195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-16786
 
14.5%
2.333615
 
< 0.1%
2.337615
 
< 0.1%
2.396915
 
< 0.1%
2.317214
 
< 0.1%
2.281614
 
< 0.1%
2.354614
 
< 0.1%
2.294613
 
< 0.1%
2.337213
 
< 0.1%
2.351913
 
< 0.1%
Other values (13119)39739
85.2%
ValueCountFrequency (%)
-16786
14.5%
1.03271
 
< 0.1%
1.03991
 
< 0.1%
1.06431
 
< 0.1%
1.08171
 
< 0.1%
1.08481
 
< 0.1%
1.10771
 
< 0.1%
1.10881
 
< 0.1%
1.12771
 
< 0.1%
1.13981
 
< 0.1%
ValueCountFrequency (%)
4.711
< 0.1%
4.48271
< 0.1%
4.38791
< 0.1%
4.37061
< 0.1%
4.34491
< 0.1%
4.28381
< 0.1%
4.25461
< 0.1%
4.2151
< 0.1%
4.17571
< 0.1%
4.12761
< 0.1%

PupilValidityRight
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size364.6 KiB
1
39865 
0
6786 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
139865
85.5%
06786
 
14.5%

Length

2022-06-04T08:13:11.521225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T08:13:11.581241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
139865
85.5%
06786
 
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size364.6 KiB
Estudio
31207 
Control
15444 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowControl
2nd rowControl
3rd rowControl
4th rowControl
5th rowControl

Common Values

ValueCountFrequency (%)
Estudio31207
66.9%
Control15444
33.1%

Length

2022-06-04T08:13:11.649258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T08:13:11.713275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
estudio31207
66.9%
control15444
33.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diagnosis
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size364.6 KiB
ACV
17604 
Control
15444 
TCE
13603 

Length

Max length7
Median length3
Mean length4.324215987
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowControl
2nd rowControl
3rd rowControl
4th rowControl
5th rowControl

Common Values

ValueCountFrequency (%)
ACV17604
37.7%
Control15444
33.1%
TCE13603
29.2%

Length

2022-06-04T08:13:11.786159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T08:13:11.858912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
acv17604
37.7%
control15444
33.1%
tce13603
29.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-06-04T08:13:06.156396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:54.659780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.911264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.147010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.456621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.693964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.314494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.522729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.763359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.919498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:06.293809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:54.795814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.051518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.312034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.580920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.854402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.463337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.659855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.899950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.068835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:06.401837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:54.883848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.143325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.412552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.685999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.958867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.567651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.752214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.999976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.172862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:06.519373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.061561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.267358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.567960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.810039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:00.111934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.696871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.904994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.108260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.317867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:06.956279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.181265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.388679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.709425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.934352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:00.232327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.814615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.029449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.217095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.438091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:07.069292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.297543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.539738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.830525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.050382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:00.369032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.948276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.161711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.332904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.559283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:07.189553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.406378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.697242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.942747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.162403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:00.826131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.060232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.289745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.462554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.678890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:07.305583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.534410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.809530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.071504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.299634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:00.954402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.192487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.429708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.570826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.791169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:07.422863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.638940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:56.922477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.223544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.445455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.066429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.298201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.533971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.678853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:05.903199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:07.543139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:55.770772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:57.042729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:58.342029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:12:59.568550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:01.186745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:02.406699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:03.647124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:04.799479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T08:13:06.028139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-04T08:13:11.938440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-04T08:13:12.131307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-04T08:13:12.301778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-04T08:13:12.460534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-04T08:13:12.584567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-04T08:13:07.719182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-04T08:13:08.064176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idTimeStampAgeGazePointXLeftGazePointYLeftValidityLeftGazePointXRightGazePointYRightValidityRightGazePointXGazePointYPupilSizeLeftPupilValidityLeftPupilSizeRightPupilValidityRightclassdiagnosis
01-1688.37456-1-10-1-10-1-1-1.00000-1.00000ControlControl
11-1688.84256-1-10-1-10-1-1-1.00000-1.00000ControlControl
21-1704.48756875549177128418234162.156512.15751ControlControl
31-1723.02956-1-10-1-10-1-1-1.00000-1.00000ControlControl
41-1750.04056879519179624518383822.044912.15281ControlControl
51-1776.03256898547178531618424312.119912.18331ControlControl
61-1803.488568885251-1-108885252.07671-1.00000ControlControl
71-1831.13556882563176832218254422.103912.32151ControlControl
81-1857.07556879513178936218344372.049012.46361ControlControl
91-1883.20556915507179630118564042.061912.26281ControlControl

Last rows

idTimeStampAgeGazePointXLeftGazePointYLeftValidityLeftGazePointXRightGazePointYRightValidityRightGazePointXGazePointYPupilSizeLeftPupilValidityLeftPupilSizeRightPupilValidityRightclassdiagnosis
4664112-354254.10017216896123188112248882.203412.26951EstudioTCE
4664212-354354.65717290931118484412378872.355612.12651EstudioTCE
4664312-354355.14417229943118284112068922.229112.24581EstudioTCE
4664412-354355.58117193911119188111928962.310312.24941EstudioTCE
4664512-354356.04617117931115984911388902.580712.20221EstudioTCE
4664612-354379.20917162808126394412138762.645312.48851EstudioTCE
4664712-354406.85017136816127296912048922.618912.49061EstudioTCE
4664812-354433.94617122942119185411578982.568412.27741EstudioTCE
4664912-354460.92617326918136493613459272.356212.67051EstudioTCE
4665012-354487.08017354938136398813599632.250712.55881EstudioTCE